Human brain state dynamics are highly reproducible and associated with neural and behavioral features

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatiotemporal features of co-activation patterns at the single-subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

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Response: We submit Supplementary Information files underlying the Figures 3, 5 S2 Data.Data used to generate Figures 5 and 6.Sheet 1 (Behavioral PC Loadings) involves the loadings of 262 behavioral variables (columns) on the behavioral PCs 1-15 (rows).Sheet 2 (Individual PC scores) involves the scores of individual subjects (rows) for the behavioral PCs 1-15 (columns).Finally, we included the following text in the section "Data Availability" of the main manuscript file and the Data Accessibility Statement on the submission system.In addition, the codes used in this study have been published on Zenodo as per the guideline.

S3
Data.Data used to generate Figure 6, S15 Fig and S16 Fig. (Sheets 1-3) Results in Figure 6 and S16 Fig. (Sheet 4-6) Null data results in S15 Fig and and S16 Fig. Columns A-G involve results from the first analysis using split-half datasets across 1,000 permutations.Column A (permutation index).Columns B-F: Partial R 2 estimated for neural PC 1, neural PC 2, neural PC 3, age and sex.Column G: Overall R 2 .Columns H-J involve results from the second analysis using cross-validation, where the multiple linear regression model from Split 1 data was used for predicting individual behavioral PC scores in the corresponding split 2 data across permutations.Column H: Correlation coefficient.Column I: p-value estimated for the correlation coefficient.Column J: Overall R 2 .S4 Data.Data used to generate Figure 7. Column A (subgroup_30nf): Subgroup identified for each individual using 30 neural features.Column B (subject ID).Column C (k5-k4_counts_mean12_z): Probability of CAP III occurrence (z).Column D-F.Individual scores on neural PCs 1-3.Column G. Individual scores on behavioral PC 1. S5 Data.Data used to generate S17 Fig. (Sheets 1-3) Results excluding subjects with high motion.(Sheet 4-6) Null data results excluding subjects with high motion.

Fig 3 .
Fig 3. "The data used to generate the results can be found in S1 Data."Fig 5. "The data used to generate the results can be found in S2 Data."Fig 6. "The data used to generate the results can be found in S2 Data and S3 Data."Fig 7. "The data used to generate the results can be found in S4 Data."S16 Fig. "The data used to generate the results can be found in S3 Data."S17 Fig. "The data used to generate the results can be found in S5 Data."